{"id":38768,"date":"2024-12-26T08:45:12","date_gmt":"2024-12-26T08:45:12","guid":{"rendered":"https:\/\/www.railscarma.com\/?p=38768"},"modified":"2026-01-01T05:34:43","modified_gmt":"2026-01-01T05:34:43","slug":"10-algorithmes-dapprentissage-automatique-a-connaitre","status":"publish","type":"post","link":"https:\/\/www.railscarma.com\/fr\/blog\/10-algorithmes-dapprentissage-automatique-a-connaitre\/","title":{"rendered":"Top 10 Machine Learning Algorithms to Know in 2026"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"38768\" class=\"elementor elementor-38768\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-aa343f4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"aa343f4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-95f1af0\" data-id=\"95f1af0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-abc854b elementor-widget elementor-widget-text-editor\" data-id=\"abc854b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Machine Learning (ML) continues to be a transformative technology across industries in 2026, influencing healthcare, finance, <a href=\"https:\/\/www.railscarma.com\/fr\/spree-commerce-development\/\">Commerce \u00e9lectronique<\/a>et les syst\u00e8mes autonomes. Au c\u0153ur de la ML se trouvent ses algorithmes, qui permettent aux ordinateurs d'apprendre \u00e0 partir de donn\u00e9es et de prendre des d\u00e9cisions sans programmation explicite. Que vous soyez un scientifique des donn\u00e9es, un ing\u00e9nieur ou un passionn\u00e9, la compr\u00e9hension de ces algorithmes vous aidera \u00e0 naviguer dans le paysage de la ML.\u00a0<\/span><\/p><h2><b>Qu'est-ce que l'apprentissage profond ?<\/b><\/h2><p><span style=\"font-weight: 400;\">L'apprentissage profond est un sous-ensemble de l'apprentissage automatique, qui est lui-m\u00eame une branche de l'apprentissage automatique. <a href=\"https:\/\/www.railscarma.com\/fr\/enterprise-ai-development-company\/\">l'intelligence artificielle (IA)<\/a>. L'apprentissage en profondeur utilise des r\u00e9seaux neuronaux artificiels con\u00e7us pour imiter la mani\u00e8re dont le cerveau humain traite et apprend des informations. Ces r\u00e9seaux sont structur\u00e9s en couches qui traitent les donn\u00e9es de mani\u00e8re de plus en plus complexe, ce qui permet aux machines d'effectuer des t\u00e2ches telles que la reconnaissance d'images, <a href=\"https:\/\/www.railscarma.com\/fr\/services-de-traitement-du-langage-naturel\/\">traitement du langage naturel<\/a>et la synth\u00e8se vocale avec une pr\u00e9cision remarquable.<\/span><\/p><h3><b>Caract\u00e9ristiques principales de l'apprentissage en profondeur :<\/b><\/h3><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>R\u00e9seaux neuronaux en couches<\/b><span style=\"font-weight: 400;\">:<\/span><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">L'apprentissage profond utilise des r\u00e9seaux neuronaux \u00e0 plusieurs couches, souvent appel\u00e9s \"r\u00e9seaux neuronaux profonds\". Chaque couche extrait des caract\u00e9ristiques de plus haut niveau des donn\u00e9es d'entr\u00e9e, ce qui permet une compr\u00e9hension et une prise de d\u00e9cision sophistiqu\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Apprentissage des caract\u00e9ristiques<\/b><span style=\"font-weight: 400;\">:<\/span><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">Contrairement \u00e0 l'apprentissage automatique traditionnel, les mod\u00e8les d'apprentissage profond peuvent apprendre automatiquement des caract\u00e9ristiques \u00e0 partir de donn\u00e9es brutes sans n\u00e9cessiter d'extraction manuelle. Cela les rend particuli\u00e8rement utiles pour traiter les donn\u00e9es non structur\u00e9es telles que les images, l'audio et le texte.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Exigences en mati\u00e8re de donn\u00e9es volumineuses<\/b><span style=\"font-weight: 400;\">:<\/span><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">L'apprentissage profond se d\u00e9veloppe sur de grands ensembles de donn\u00e9es, car la grande quantit\u00e9 de donn\u00e9es aide les r\u00e9seaux neuronaux \u00e0 atteindre une meilleure pr\u00e9cision en apprenant des mod\u00e8les complexes.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Puissance de calcul \u00e9lev\u00e9e<\/b><span style=\"font-weight: 400;\">:<\/span><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">La formation de mod\u00e8les d'apprentissage profond n\u00e9cessite d'importantes ressources informatiques, notamment des GPU (unit\u00e9s de traitement graphique) ou des TPU (unit\u00e9s de traitement tensoriel), pour traiter efficacement les donn\u00e9es.<\/span><\/li><\/ol><h3><b>Applications de l'apprentissage profond :<\/b><\/h3><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Reconnaissance d'images et de vid\u00e9os<\/b><span style=\"font-weight: 400;\">: Utilis\u00e9 dans les syst\u00e8mes de reconnaissance faciale, l'imagerie m\u00e9dicale et les v\u00e9hicules autonomes.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Traitement du langage naturel (NLP)<\/b><span style=\"font-weight: 400;\">: Permet d'alimenter des applications telles que les chatbots, la traduction linguistique et l'analyse des sentiments.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Reconnaissance de la parole<\/b><span style=\"font-weight: 400;\">: Active les assistants virtuels tels que Siri, Alexa et Google Assistant.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Mod\u00e8les g\u00e9n\u00e9ratifs<\/b><span style=\"font-weight: 400;\">: Cr\u00e9e du contenu comme des vid\u00e9os deepfake, de l'art et de la musique.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Soins de sant\u00e9<\/b><span style=\"font-weight: 400;\">: Aide au diagnostic, \u00e0 la d\u00e9couverte de m\u00e9dicaments et \u00e0 l'\u00e9laboration de plans de traitement personnalis\u00e9s.<\/span><\/li><\/ul><h3><b>Cadres populaires d'apprentissage profond :<\/b><\/h3><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>TensorFlow<\/b><span style=\"font-weight: 400;\">: D\u00e9velopp\u00e9 par Google, il est largement utilis\u00e9 pour la construction et l'entra\u00eenement de mod\u00e8les d'apprentissage profond.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>PyTorch<\/b><span style=\"font-weight: 400;\">: Une biblioth\u00e8que open-source appr\u00e9ci\u00e9e des chercheurs et des d\u00e9veloppeurs pour son graphe de calcul dynamique.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Keras<\/b><span style=\"font-weight: 400;\">: Une API de haut niveau construite au-dessus de TensorFlow, facilitant la conception et l'entra\u00eenement de mod\u00e8les d'apprentissage profond.<\/span><\/li><\/ol><h3><b>L'avenir de l'apprentissage profond :<\/b><\/h3><p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.carmatec.com\/deep-learning-company\/\">Apprentissage en profondeur<\/a> devrait continuer \u00e0 cro\u00eetre, permettant des avanc\u00e9es dans des domaines tels que la robotique, la mod\u00e9lisation climatique et les syst\u00e8mes autonomes. Gr\u00e2ce aux innovations en cours dans le domaine du mat\u00e9riel informatique et de l'efficacit\u00e9 des algorithmes, l'accessibilit\u00e9 et l'impact de cette technologie ne peuvent que s'accro\u00eetre.<\/span><\/p><h2><b>What are the 10 Machine Learning Algorithms to Know in 2026?<\/b><\/h2><p><span style=\"font-weight: 400;\">Here are the top 10 machine learning algorithms you need to know in 2026, explained in detail:<\/span><\/p><ol><li><b> R\u00e9gression lin\u00e9aire<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">La r\u00e9gression lin\u00e9aire est l'un des algorithmes d'apprentissage supervis\u00e9 les plus simples et les plus puissants. Il mod\u00e9lise la relation lin\u00e9aire entre les caract\u00e9ristiques d'entr\u00e9e (variables ind\u00e9pendantes) et une variable cible (variable d\u00e9pendante).<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Il minimise la somme des diff\u00e9rences quadratiques entre les valeurs pr\u00e9dites et les valeurs r\u00e9elles.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Interpr\u00e9table et rapide. Id\u00e9al pour les petits ensembles de donn\u00e9es avec des relations lin\u00e9aires.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Pr\u00e9vision des ventes, des prix de l'immobilier et des tendances en mati\u00e8re de temp\u00e9rature.<\/span><\/li><\/ul><ol start=\"2\"><li><b> R\u00e9gression logistique<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Malgr\u00e9 son nom, la r\u00e9gression logistique est un algorithme de classification. Elle pr\u00e9dit des r\u00e9sultats cat\u00e9goriques, tels que \"oui\" ou \"non\", en estimant les probabilit\u00e9s \u00e0 l'aide d'une fonction sigmo\u00efde.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Applique une transformation logit pour pr\u00e9dire des r\u00e9sultats binaires.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Robuste pour les t\u00e2ches de classification binaire, facile \u00e0 mettre en \u0153uvre et interpr\u00e9table.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: D\u00e9tection de spam, approbation de cr\u00e9dit et pr\u00e9diction du d\u00e9sabonnement des clients.<\/span><\/li><\/ul><ol start=\"3\"><li><b> Arbres de d\u00e9cision<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les arbres de d\u00e9cision divisent les donn\u00e9es en sous-ensembles bas\u00e9s sur les valeurs des caract\u00e9ristiques, cr\u00e9ant ainsi une structure arborescente pour la prise de d\u00e9cision. Ils sont intuitifs et efficaces pour les t\u00e2ches de classification et de r\u00e9gression.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Sur la base de l'impuret\u00e9 de Gini ou du gain d'information pour diviser les n\u0153uds.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Facile \u00e0 visualiser et \u00e0 interpr\u00e9ter ; traite les donn\u00e9es num\u00e9riques et cat\u00e9gorielles.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Pr\u00e9diction de l'\u00e9ligibilit\u00e9 \u00e0 un pr\u00eat, d\u00e9tection de la fraude et diagnostic m\u00e9dical.<\/span><\/li><\/ul><ol start=\"4\"><li><b> For\u00eats al\u00e9atoires<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les for\u00eats al\u00e9atoires sont un ensemble d'arbres de d\u00e9cision qui am\u00e9liorent la pr\u00e9cision et r\u00e9duisent l'ajustement excessif en calculant la moyenne des pr\u00e9dictions. Elles sont robustes et polyvalentes.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Cr\u00e9e plusieurs arbres de d\u00e9cision \u00e0 l'aide d'un \u00e9chantillonnage al\u00e9atoire de donn\u00e9es et de caract\u00e9ristiques.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Grande pr\u00e9cision, traitement des donn\u00e9es manquantes et r\u00e9duction de l'ajustement excessif.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Segmentation de la client\u00e8le, pr\u00e9diction du cours des actions et analyse marketing.<\/span><\/li><\/ul><ol start=\"5\"><li><b> Machines \u00e0 vecteurs de support (SVM)<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Le SVM est un algorithme d'apprentissage supervis\u00e9 utilis\u00e9 pour la classification et la r\u00e9gression. Il fonctionne en trouvant l'hyperplan qui s\u00e9pare le mieux les points de donn\u00e9es en diff\u00e9rentes classes.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Maximise la marge entre les classes tout en minimisant les erreurs de classification.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Efficace dans les espaces \u00e0 haute dimension et les limites de d\u00e9cision non lin\u00e9aires.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Reconnaissance des visages, cat\u00e9gorisation des textes et classification des images.<\/span><\/li><\/ul><ol start=\"6\"><li><b> Voisins les plus proches (K-Nearest Neighbors - KNN)<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">KNN est un algorithme d'apprentissage simple, bas\u00e9 sur des instances, qui classe les points de donn\u00e9es en fonction de leurs voisins les plus proches.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Mesure les distances (par exemple, Euclide) pour trouver les k-voisins les plus proches et assigne la classe majoritaire.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Non param\u00e9trique et simple \u00e0 comprendre.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Syst\u00e8mes de recommandation, reconnaissance des formes et d\u00e9tection des anomalies.<\/span><\/li><\/ul><ol start=\"7\"><li><b> Machines de renforcement du gradient (GBM)<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les GBM sont des m\u00e9thodes d'ensemble qui construisent des mod\u00e8les de mani\u00e8re s\u00e9quentielle, en corrigeant les erreurs commises par les mod\u00e8les pr\u00e9c\u00e9dents. Les impl\u00e9mentations les plus r\u00e9pandues sont XGBoost, LightGBM et CatBoost.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Utilise la descente de gradient pour minimiser les fonctions de perte de mani\u00e8re it\u00e9rative.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Haute pr\u00e9cision et largement utilis\u00e9e dans les t\u00e2ches concurrentielles de ML.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: D\u00e9tection des fraudes, pr\u00e9diction du taux de clics et segmentation de la client\u00e8le.<\/span><\/li><\/ul><ol start=\"8\"><li><b> R\u00e9seaux neuronaux<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les r\u00e9seaux neuronaux imitent le cerveau humain en utilisant des couches de n\u0153uds interconnect\u00e9s (neurones). Ils excellent dans la mod\u00e9lisation de relations complexes dans de grands ensembles de donn\u00e9es.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Utilise la r\u00e9tropropagation pour ajuster les poids et minimiser l'erreur.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Traite efficacement les donn\u00e9es non structur\u00e9es telles que le texte, les images et le son.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: NLP, reconnaissance d'images, conduite autonome et syst\u00e8mes de conversion de la parole en texte.<\/span><\/li><\/ul><ol start=\"9\"><li><b> Regroupement K-Means<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">K-means est un algorithme d'apprentissage non supervis\u00e9 utilis\u00e9 pour regrouper des donn\u00e9es en groupes sur la base de leur similarit\u00e9.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Affecte it\u00e9rativement les points aux grappes et minimise la variance intra-grappe.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Simple \u00e0 mettre en \u0153uvre et efficace pour les grands ensembles de donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Segmentation de la client\u00e8le, regroupement de documents et analyse de donn\u00e9es g\u00e9ospatiales.<\/span><\/li><\/ul><ol start=\"10\"><li><b> Apprentissage par renforcement<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">L'apprentissage par renforcement (RL) forme des agents \u00e0 prendre des d\u00e9cisions s\u00e9quentielles en interagissant avec un environnement et en recevant un retour d'information sous forme de r\u00e9compenses ou de p\u00e9nalit\u00e9s.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Math\u00e9matiques<\/b><span style=\"font-weight: 400;\">: Bas\u00e9 sur les processus de d\u00e9cision de Markov (PDM) et les techniques d'optimisation.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Points forts<\/b><span style=\"font-weight: 400;\">: Excelle dans les t\u00e2ches n\u00e9cessitant une prise de d\u00e9cision s\u00e9quentielle.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cas d'utilisation<\/b><span style=\"font-weight: 400;\">: Robotique, jeux (p. ex. AlphaGo) et recommandations personnalis\u00e9es.<\/span><\/li><\/ul><h2><b>Types d'algorithmes d'apprentissage automatique<\/b><\/h2><p><span style=\"font-weight: 400;\">Les algorithmes d'apprentissage automatique sont principalement class\u00e9s en trois types en fonction de la mani\u00e8re dont ils apprennent \u00e0 partir des donn\u00e9es :<\/span><\/p><ol><li><b> Algorithmes d'apprentissage supervis\u00e9<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">L'apprentissage supervis\u00e9 n\u00e9cessite des ensembles de donn\u00e9es \u00e9tiquet\u00e9es, o\u00f9 chaque entr\u00e9e est associ\u00e9e \u00e0 la sortie correspondante. L'algorithme apprend \u00e0 mettre en correspondance les entr\u00e9es et les sorties et pr\u00e9dit les r\u00e9sultats pour les nouvelles donn\u00e9es.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cas d'utilisation : Pr\u00e9vision des prix de l'immobilier, d\u00e9tection des spams et des fraudes.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Exemples d'algorithmes :<\/span><ul><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">R\u00e9gression lin\u00e9aire<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">R\u00e9gression logistique<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Arbres de d\u00e9cision<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Machines \u00e0 vecteurs de support (SVM)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">R\u00e9seaux neuronaux<\/span><\/li><\/ul><\/li><\/ul><ol start=\"2\"><li><b> Algorithmes d'apprentissage non supervis\u00e9<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">L'apprentissage non supervis\u00e9 fonctionne avec des donn\u00e9es non \u00e9tiquet\u00e9es. L'algorithme identifie des mod\u00e8les, des structures ou des regroupements au sein de l'ensemble de donn\u00e9es.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cas d'utilisation : Segmentation de la client\u00e8le, d\u00e9tection des anomalies et syst\u00e8mes de recommandation.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Exemples d'algorithmes :<\/span><ul><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Regroupement K-Means<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Analyse en composantes principales (ACP)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Regroupement hi\u00e9rarchique<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Autoencodeurs<\/span><\/li><\/ul><\/li><\/ul><ol start=\"3\"><li><b> Algorithmes d'apprentissage par renforcement<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">L'apprentissage par renforcement se concentre sur la formation d'agents qui prennent des d\u00e9cisions s\u00e9quentielles en interagissant avec un environnement. L'agent apprend par essais et erreurs \u00e0 maximiser les r\u00e9compenses au fil du temps.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cas d'utilisation : Jeux (comme AlphaGo), robotique et conduite autonome.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Exemples d'algorithmes :<\/span><ul><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Q-Learning<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">R\u00e9seaux Q profonds (DQN)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Optimisation de la politique proximale (PPO)<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">M\u00e9thodes de Monte Carlo<\/span><\/li><\/ul><\/li><\/ul><h2><b>Why These Algorithms Matter in 2026<\/b><\/h2><ol><li style=\"font-weight: 400;\" aria-level=\"1\"><b>\u00c9volutivit\u00e9<\/b><span style=\"font-weight: 400;\">: Algorithms like random forests and GBMs efficiently handle large datasets, a growing need in 2026.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Polyvalence<\/b><span style=\"font-weight: 400;\">: Qu'il s'agisse de donn\u00e9es structur\u00e9es ou non structur\u00e9es, ces algorithmes r\u00e9pondent \u00e0 divers probl\u00e8mes commerciaux.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Outils \u00e9mergents<\/b><span style=\"font-weight: 400;\">: Des frameworks comme TensorFlow et Scikit-learn simplifient leur mise en \u0153uvre, ce qui les rend accessibles.<\/span><\/li><\/ol><h2><b>Comment fonctionnent les algorithmes d'apprentissage profond ?<\/b><\/h2><p><span style=\"font-weight: 400;\">Les algorithmes d'apprentissage en profondeur fonctionnent en imitant la structure et les op\u00e9rations du cerveau humain par le biais de r\u00e9seaux neuronaux artificiels. Ces algorithmes apprennent des mod\u00e8les et des relations dans les donn\u00e9es en les faisant passer par plusieurs couches de n\u0153uds interconnect\u00e9s, ou neurones, dans un r\u00e9seau. Voici une description d\u00e9taill\u00e9e de leur fonctionnement :<\/span><\/p><ol><li><b> Entr\u00e9e des donn\u00e9es<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les mod\u00e8les d'apprentissage profond n\u00e9cessitent de grandes quantit\u00e9s de donn\u00e9es pour l'entra\u00eenement. Ces donn\u00e9es peuvent \u00eatre structur\u00e9es (comme des tableaux) ou non structur\u00e9es (comme des images, du son ou du texte). Par exemple, les mod\u00e8les d'apprentissage profond n\u00e9cessitent de grandes quantit\u00e9s de donn\u00e9es pour la formation :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dans la reconnaissance d'images, les donn\u00e9es peuvent \u00eatre des images d'objets \u00e9tiquet\u00e9s.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dans le cas de la reconnaissance vocale, les donn\u00e9es d'entr\u00e9e peuvent \u00eatre des fichiers audio associ\u00e9s \u00e0 des transcriptions textuelles.<\/span><\/li><\/ul><ol start=\"2\"><li><b> R\u00e9seaux neuronaux artificiels<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les r\u00e9seaux de neurones artificiels (ANN) sont au c\u0153ur de l'apprentissage profond. Ces r\u00e9seaux sont constitu\u00e9s de :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Couche d'entr\u00e9e : L\u00e0 o\u00f9 les donn\u00e9es entrent dans le r\u00e9seau.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Couches cach\u00e9es : Couches multiples entre les couches d'entr\u00e9e et de sortie, responsables du traitement des donn\u00e9es. Ces couches sont \"profondes\", d'o\u00f9 le nom d'apprentissage en profondeur.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Couche de sortie : La couche finale qui fournit des pr\u00e9dictions ou des classifications bas\u00e9es sur les mod\u00e8les appris.<\/span><\/li><\/ul><ol start=\"3\"><li><b> Propagation vers l'avant<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Les donn\u00e9es circulent dans le r\u00e9seau selon un processus appel\u00e9 propagation vers l'avant :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Chaque neurone d'une couche re\u00e7oit des donn\u00e9es de la couche pr\u00e9c\u00e9dente.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Une somme pond\u00e9r\u00e9e des entr\u00e9es est calcul\u00e9e et passe par une fonction d'activation (comme ReLU, Sigmo\u00efde ou Tanh) pour introduire la non-lin\u00e9arit\u00e9.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">La sortie d'une couche sert d'entr\u00e9e \u00e0 la suivante.<\/span><\/li><\/ul><ol start=\"4\"><li><b> Fonction de perte<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Une fois que le mod\u00e8le a fait une pr\u00e9diction, une fonction de perte \u00e9value la diff\u00e9rence entre la sortie pr\u00e9dite et la valeur r\u00e9elle (v\u00e9rit\u00e9 de terrain). La fonction de perte fournit une valeur num\u00e9rique repr\u00e9sentant l'erreur du mod\u00e8le.<\/span><\/p><ol start=\"5\"><li><b> Propagation \u00e0 rebours<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Pour am\u00e9liorer la pr\u00e9cision, le mod\u00e8le ajuste ses param\u00e8tres internes (poids et biais) par r\u00e9tropropagation :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les gradients de la fonction de perte sont calcul\u00e9s en fonction des param\u00e8tres du mod\u00e8le \u00e0 l'aide de la diff\u00e9renciation automatique.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Ces gradients sont utilis\u00e9s pour mettre \u00e0 jour les poids et les biais par le biais d'un algorithme d'optimisation (g\u00e9n\u00e9ralement la descente stochastique de gradient ou l'optimiseur d'Adam).<\/span><\/li><\/ul><ol start=\"6\"><li><b> Formation<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Le mod\u00e8le r\u00e9p\u00e8te les processus de propagation vers l'avant et vers l'arri\u00e8re plusieurs fois sur de nombreuses \u00e9poques (it\u00e9rations sur l'ensemble des donn\u00e9es). Chaque it\u00e9ration permet d'affiner les poids afin de r\u00e9duire l'erreur et d'am\u00e9liorer les performances.<\/span><\/p><ol start=\"7\"><li><b> Essais et validation<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Une fois form\u00e9, le mod\u00e8le est test\u00e9 sur des donn\u00e9es in\u00e9dites afin d'\u00e9valuer sa capacit\u00e9 de g\u00e9n\u00e9ralisation. Des mesures telles que l'exactitude, la pr\u00e9cision, le rappel ou le score F1 sont utilis\u00e9es pour mesurer les performances.<\/span><\/p><ol start=\"8\"><li><b> Pr\u00e9dictions<\/b><\/li><\/ol><p><span style=\"font-weight: 400;\">Apr\u00e8s l'entra\u00eenement et la validation, le mod\u00e8le est pr\u00eat \u00e0 faire des pr\u00e9dictions sur de nouvelles donn\u00e9es. Par exemple, le mod\u00e8le est pr\u00eat \u00e0 faire des pr\u00e9dictions sur de nouvelles donn\u00e9es :<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dans une t\u00e2che de classification d'images, il peut pr\u00e9dire si une image contient un chien ou un chat.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dans un mod\u00e8le linguistique, il peut g\u00e9n\u00e9rer du texte ou traduire des phrases.<\/span><\/li><\/ul><h3><b>Concepts fondamentaux de l'apprentissage profond :<\/b><\/h3><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Surajustement et r\u00e9gularisation : Assure que le mod\u00e8le ne m\u00e9morise pas les donn\u00e9es d'apprentissage et qu'il se g\u00e9n\u00e9ralise bien.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Abandon : Technique permettant de d\u00e9sactiver les neurones de mani\u00e8re al\u00e9atoire pendant la formation afin d'am\u00e9liorer la g\u00e9n\u00e9ralisation.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Normalisation par lots : Acc\u00e9l\u00e8re la formation et stabilise le processus d'apprentissage.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Apprentissage par transfert : R\u00e9utilisation de mod\u00e8les pr\u00e9form\u00e9s pour des t\u00e2ches similaires afin d'\u00e9conomiser du temps et des ressources.<\/span><\/li><\/ul><h2><b>Conclusion<\/b><\/h2><p><span style=\"font-weight: 400;\">Understanding these machine learning algorithms is essential for professionals to stay competitive in the evolving tech landscape. Whether you&#8217;re building predictive models, improving user experiences, or developing AI-driven solutions, mastering these techniques will empower you to unlock new opportunities in 2026 and beyond. To know more about <a href=\"https:\/\/www.railscarma.com\/fr\/societe-specialisee-dans-le-developpement-de-solutions-dapprentissage-automatique\/\">Services de d\u00e9veloppement ML<\/a> se connecter avec <a href=\"https:\/\/www.railscarma.com\/fr\">RailsCarma<\/a>.<\/span><\/p><h2><b>Questions fr\u00e9quemment pos\u00e9es<\/b><\/h2><ol><li><b> What are the most commonly used machine learning algorithms in 2026?<\/b><b><br \/><\/b><span style=\"font-weight: 400;\">Les algorithmes les plus utilis\u00e9s sont les suivants :<\/span><\/li><\/ol><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>R\u00e9gression lin\u00e9aire<\/b><span style=\"font-weight: 400;\"> et <\/span><b>R\u00e9gression logistique<\/b><span style=\"font-weight: 400;\"> pour la mod\u00e9lisation pr\u00e9dictive.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Arbres de d\u00e9cision<\/b><span style=\"font-weight: 400;\"> et <\/span><b>For\u00eats al\u00e9atoires<\/b><span style=\"font-weight: 400;\"> pour les t\u00e2ches de classification et de r\u00e9gression.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Machines \u00e0 vecteurs de support (SVM)<\/b><span style=\"font-weight: 400;\"> pour la classification des donn\u00e9es.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>R\u00e9seaux neuronaux<\/b><span style=\"font-weight: 400;\"> pour les applications d'apprentissage profond.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Voisins les plus proches (K-Nearest Neighbors - KNN)<\/b><span style=\"font-weight: 400;\"> pour le regroupement et la classification.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Algorithmes de renforcement du gradient<\/b><span style=\"font-weight: 400;\"> comme XGBoost et LightGBM pour les t\u00e2ches de haute pr\u00e9cision.<\/span><\/li><\/ul><ol start=\"2\"><li><b> How do machine learning algorithms adapt to advancements in 2026?<\/b><b><br \/><\/b><span style=\"font-weight: 400;\">In 2026, ML algorithms are evolving to handle:<\/span><\/li><\/ol><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Des ensembles de donn\u00e9es plus importants<\/b><span style=\"font-weight: 400;\"> gr\u00e2ce \u00e0 l'informatique distribu\u00e9e.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Des temps d'entra\u00eenement plus rapides<\/b><span style=\"font-weight: 400;\"> en utilisant des optimisations telles que l'acc\u00e9l\u00e9ration GPU et TPU.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Traitement en temps r\u00e9el<\/b><span style=\"font-weight: 400;\"> avec des cadres d'apprentissage en ligne.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Am\u00e9lioration de l'interpr\u00e9tabilit\u00e9<\/b><span style=\"font-weight: 400;\"> gr\u00e2ce \u00e0 des techniques d'IA explicable (XAI).<\/span><\/li><\/ul><ol start=\"3\"><li><b> Which algorithm is best for image recognition in 2026?<\/b><b><br \/><\/b><span style=\"font-weight: 400;\">Convolutional Neural Networks (CNNs) continue to be the dominant choice for image recognition tasks in 2026, thanks to their ability to process spatial hierarchies and detect patterns in image data effectively. Advanced architectures like EfficientNet and Vision Transformers (ViT) are gaining traction for complex tasks.<\/span><\/li><\/ol><ol start=\"4\"><li><b> What is the role of Reinforcement Learning in 2026?<\/b><b><br \/><\/b><span style=\"font-weight: 400;\">L'apprentissage par renforcement (RL) est essentiel pour :<\/span><\/li><\/ol><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Les syst\u00e8mes autonomes tels que les voitures auto-conduites.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Robotique et automatisation industrielle.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Mod\u00e9lisation financi\u00e8re pour une prise de d\u00e9cision dynamique.<\/span><span style=\"font-weight: 400;\"><br \/><\/span><span style=\"font-weight: 400;\">RL advancements in 2026 are supported by improved algorithms like Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO).<\/span><\/li><\/ul><ol start=\"5\"><li><b> Comment choisir l'algorithme \u00e0 utiliser pour mon projet ?<\/b><b><br \/><\/b><span style=\"font-weight: 400;\">Consid\u00e9rez ce qui suit :<\/span><\/li><\/ol><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Type de donn\u00e9es<\/b><span style=\"font-weight: 400;\">: S'agit-il de donn\u00e9es structur\u00e9es, non structur\u00e9es ou de s\u00e9ries chronologiques ?<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Objectif de la t\u00e2che<\/b><span style=\"font-weight: 400;\">: Classification, r\u00e9gression, regroupement, etc.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Complexit\u00e9<\/b><span style=\"font-weight: 400;\">: Les mod\u00e8les plus simples tels que la r\u00e9gression logistique sont plus adapt\u00e9s aux solutions interpr\u00e9tables, tandis que les r\u00e9seaux neuronaux sont plus adapt\u00e9s aux donn\u00e9es de haute dimension.<\/span><\/li><li 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src=\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/What-is-Offliberty-Ruby-Gem-and-How-It-Works.png\" class=\"attachment-full size-full wp-post-image\" alt=\"Offliberty Ruby Gem\" srcset=\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/What-is-Offliberty-Ruby-Gem-and-How-It-Works.png 800w, https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/What-is-Offliberty-Ruby-Gem-and-How-It-Works-300x113.png 300w, https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/What-is-Offliberty-Ruby-Gem-and-How-It-Works-768x288.png 768w, https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/What-is-Offliberty-Ruby-Gem-and-How-It-Works-18x7.png 18w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\r\n\r\n    <\/a>\r\n  <\/div>\r\n\r\n  <a class=\"title post_title\"  title=\"Qu&#039;est-ce que Offliberty Ruby Gem et comment fonctionne-t-il ?\" href=\"https:\/\/www.railscarma.com\/fr\/blog\/quest-ce-que-offliberty-ruby-gem-et-comment-fonctionne-t-il\/?related_post_from=41304\">\r\n        Qu'est-ce que Offliberty Ruby Gem et comment fonctionne-t-il ?  <\/a>\r\n\r\n        <\/div>\r\n              <div class=\"item\">\r\n            <div class=\"thumb post_thumb\">\r\n    <a  title=\"Comment construire une plateforme SaaS \u00e9volutive en utilisant Ruby on Rails\" href=\"https:\/\/www.railscarma.com\/fr\/blog\/comment-creer-une-plateforme-saas-evolutive-a-laide-de-ruby-on-rails\/?related_post_from=41273\">\r\n\r\n      <img decoding=\"async\" width=\"800\" height=\"300\" src=\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/Build-a-SaaS-Platform-Using-Ruby-on-Rails.png\" class=\"attachment-full size-full wp-post-image\" alt=\"Construire une plateforme SaaS avec Ruby on Rails\" srcset=\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2026\/04\/Build-a-SaaS-Platform-Using-Ruby-on-Rails.png 800w, 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});\r\n    <\/script>\r\n  <\/div>","protected":false},"excerpt":{"rendered":"<p>Machine Learning (ML) continues to be a transformative technology across industries in 2026, influencing healthcare, finance, e-commerce, and autonomous systems. At the core of ML are its algorithms, which enable computers to learn from data and make decisions without explicit programming. Whether you&#8217;re a data scientist, engineer, or enthusiast, understanding these algorithms will help you &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/www.railscarma.com\/fr\/blog\/comment-creer-une-plateforme-saas-evolutive-a-laide-de-ruby-on-rails\/\"> <span class=\"screen-reader-text\">Comment construire une plateforme SaaS \u00e9volutive en utilisant Ruby on Rails<\/span> Lire la suite \u00bb<\/a><\/p>","protected":false},"author":5,"featured_media":38777,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1224],"tags":[],"class_list":["post-38768","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Top 10 Machine Learning Algorithms to Know in 2026 - RailsCarma<\/title>\n<meta name=\"description\" content=\"Here are Top 10 machine learning algorithms in 2025: Linear Regression, Decision Trees, SVM, KNN, Neural Networks, XGBoost, and more!\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.railscarma.com\/fr\/blog\/10-algorithmes-dapprentissage-automatique-a-connaitre\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top 10 Machine Learning Algorithms to Know in 2026 - RailsCarma\" \/>\n<meta property=\"og:description\" content=\"Here are Top 10 machine learning algorithms in 2025: Linear Regression, Decision Trees, SVM, KNN, Neural Networks, XGBoost, and more!\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.railscarma.com\/fr\/blog\/10-algorithmes-dapprentissage-automatique-a-connaitre\/\" \/>\n<meta property=\"og:site_name\" content=\"RailsCarma - Ruby on Rails Development Company specializing in Offshore Development\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/RailsCarma\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-12-26T08:45:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-01T05:34:43+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2024\/12\/10-Machine-Learning-Algorithms-to-Know-in-2025.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"300\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Nikhil\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@railscarma\" \/>\n<meta name=\"twitter:site\" content=\"@railscarma\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Nikhil\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"9 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/\"},\"author\":{\"name\":\"Nikhil\",\"@id\":\"https:\/\/www.railscarma.com\/#\/schema\/person\/1aa0357392b349082303e8222c35c30c\"},\"headline\":\"Top 10 Machine Learning Algorithms to Know in 2026\",\"datePublished\":\"2024-12-26T08:45:12+00:00\",\"dateModified\":\"2026-01-01T05:34:43+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/\"},\"wordCount\":2001,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.railscarma.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.railscarma.com\/wp-content\/uploads\/2024\/12\/10-Machine-Learning-Algorithms-to-Know-in-2025.png\",\"articleSection\":[\"Blogs\"],\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/\",\"url\":\"https:\/\/www.railscarma.com\/blog\/top-10-machine-learning-algorithms-to-know\/\",\"name\":\"Top 10 Machine Learning Algorithms to Know in 2026 - 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